Suppr超能文献

人工免疫细胞,AI 细胞,一种新工具,用于预测外周血单核细胞对核酸纳米颗粒的反应产生干扰素。

Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles.

机构信息

Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA.

National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, 20850, USA.

出版信息

Small. 2022 Nov;18(46):e2204941. doi: 10.1002/smll.202204941. Epub 2022 Oct 10.

Abstract

Nucleic acid nanoparticles, or NANPs, rationally designed to communicate with the human immune system, can offer innovative therapeutic strategies to overcome the limitations of traditional nucleic acid therapies. Each set of NANPs is unique in their architectural parameters and physicochemical properties, which together with the type of delivery vehicles determine the kind and the magnitude of their immune response. Currently, there are no predictive tools that would reliably guide the design of NANPs to the desired immunological outcome, a step crucial for the success of personalized therapies. Through a systematic approach investigating physicochemical and immunological profiles of a comprehensive panel of various NANPs, the research team developes and experimentally validates a computational model based on the transformer architecture able to predict the immune activities of NANPs. It is anticipated that the freely accessible computational tool that is called an "artificial immune cell," or AI-cell, will aid in addressing the current critical public health challenges related to safety criteria of nucleic acid therapies in a timely manner and promote the development of novel biomedical tools.

摘要

核酸纳米颗粒(NANPs),经合理设计与人体免疫系统相互作用,可以为克服传统核酸疗法的局限性提供创新的治疗策略。每组 NANPs 在其结构参数和物理化学性质方面都是独特的,这些特性与递送载体的类型一起决定了它们免疫反应的类型和程度。目前,还没有可靠的预测工具可以指导 NANPs 的设计以达到预期的免疫学效果,这是个性化治疗成功的关键步骤。通过系统地研究各种 NANPs 的物理化学和免疫学特性,研究团队开发并通过实验验证了一种基于变压器架构的计算模型,该模型能够预测 NANPs 的免疫活性。预计,这个被称为“人工免疫细胞”(AI-cell)的免费计算工具将有助于及时应对与核酸疗法安全性标准相关的当前重大公共卫生挑战,并促进新型生物医学工具的发展。

相似文献

7
Structure and Composition Define Immunorecognition of Nucleic Acid Nanoparticles.结构和组成决定核酸纳米颗粒的免疫识别。
Nano Lett. 2018 Jul 11;18(7):4309-4321. doi: 10.1021/acs.nanolett.8b01283. Epub 2018 Jun 20.

引用本文的文献

1
Q&A Translational Cancer Nanomedicine.问答:转化癌症纳米医学
Nat Commun. 2025 Sep 16;16(1):8288. doi: 10.1038/s41467-025-63488-x.

本文引用的文献

6
MolGPT: Molecular Generation Using a Transformer-Decoder Model.MolGPT:基于 Transformer-Decoder 模型的分子生成。
J Chem Inf Model. 2022 May 9;62(9):2064-2076. doi: 10.1021/acs.jcim.1c00600. Epub 2021 Oct 25.
8
Interpretable machine learning for genomics.基因组学可解释的机器学习。
Hum Genet. 2022 Sep;141(9):1499-1513. doi: 10.1007/s00439-021-02387-9. Epub 2021 Oct 20.
9
Machine Learning in Drug Discovery: A Review.药物发现中的机器学习:综述
Artif Intell Rev. 2022;55(3):1947-1999. doi: 10.1007/s10462-021-10058-4. Epub 2021 Aug 11.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验